During a snowfall, following a snowplow can be extremely dangerous. This danger comes from the human visual
system's inability to accurately perceive the speed and motion of the snowplow, often resulting in rear-end
collisions. For this project, the researchers' goal is to use their understanding of how the human visual system
processes optical motion under the conditions created by blowing snow to create a simulation framework that could
be used to test emergency lighting configurations that reduce rear-end collisions with snowplows. Reaction times
for detecting the motion of the snowplow will be measured empirically for a variety of color set-ups on a simulated
snowplow that slows down while driving on a virtual road with curves and hills. The simulated driving
environment will utilize a head-mounted, virtual reality display to render an improved snow cloud model behind
the snowplow. This driving simulator environment will serve as the basis for testing the effects of color and
lighting alternatives on snowplows. The results of this work will move the researchers closer to determining
optimal color and lighting configurations on actual snowplows.